deciqAI Bayesian Reasoning
Overview
Bayes' theorem: Posterior odds = Prior odds × Likelihood ratio. The strength of belief after evidence equals the strength before, multiplied by how diagnostic the evidence is.
This skill applies Bayesian discipline where people reason about probabilities informally — and failures follow predictable patterns: ignoring the base rate (prior), confusing P(E|H) with P(H|E) (prosecutor's fallacy), over-updating on vivid confirming evidence, treating correlated evidence as independent.
Composes with probabilistic-thinking (Bayes is the operational engine), critical-thinking (formalizes considering alternatives), logical-fallacies (prosecutor's fallacy and base-rate neglect), and first-principles (the prior is bedrock).
When to Use
- High-stakes decision rests on interpreting evidence (medical test, security alert, fraud flag, hiring signal, A/B result)
- "Evidence is consistent with X" is being treated as proof of X
- Base rates ignored — a rare event treated as probable because evidence "looks like" it
- Correlated evidence pieces treated as independent updates
- A benchmark score, AI-capability claim, AI-adoption stat, or AI-capex/valuation figure is being treated as proof without asking how often that signal appears when the underlying claim is false
- Someone says "Bayesian," "prior," "posterior," "base rate," "likelihood ratio," "update"
Not when: genuinely deterministic; no data to anchor a prior; cost of formal update exceeds the value of being more right.
Coaching Novices (Adaptive Front Door)
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output that step's question and nothing more.
- One-line: before believing what the evidence says, ask how common this situation is to start with (prior) and how often evidence would look this way even if the hypothesis is wrong.
- Check fit. Deterministic problems / no prior anchor → not this lens.
- Elicit the real claim and the evidence. What exactly are you deciding, and what evidence do you have?
[WAIT — do not advance until user responds]
- One question at a time: what's the base rate? what would evidence look like if the hypothesis were false? how much should that change your belief?
[WAIT — do not advance until user responds]
- Close: specific posterior probability, plus what next evidence would change it most.
[WAIT — do not advance until user responds]
The Process
Step 1 — Name hypothesis and alternatives. H vs. not-H must be exhaustive.
Step 2 — Anchor the prior before evidence. P(H) = base rate in the relevant population. Most failures happen here. Examples: disease prevalence (<0.1% for rare conditions); historical fraction of great hires (20-40%); alerts that proved real (<5%); Series A → $1B outcomes (~5%).
Step 3 — Estimate the likelihood ratio. LR = P(E|H) / P(E|not-H). LR > 1 supports H; LR < 1 supports not-H; LR ≈ 1 is non-diagnostic. If you cannot articulate P(E|not-H), you have half the story.
Step 4 — Compute the posterior. Prior odds × LR = Posterior odds → convert back to probability. Example: 0.1% prevalence, LR = 99 → posterior ≈ 9%. A "highly accurate" test on a rare disease still gives 91% chance of no disease on a positive result.
Step 5 — Check evidence dependence. Correlated evidence (three witnesses from the same source) should be treated as ~one piece, not compounded.
Step 6 — Commit and act. State the posterior number, the action threshold, and what next evidence would most move it.
Output: Bayesian Update
# Bayesian Update: <decision>
H / not-H:
Prior P(H): Source:
Evidence E:
P(E|H): P(E|not-H): LR:
Posterior P(H|E): Interpretation:
Independence check:
Decision threshold: Action: Next evidence:
→ Method in Action: Sally Clark Case (1999) · Air France 447 Search (2009–2011) → 2026 lens: Updating Beliefs About AI Capability and Safety From Eval Results (2023–2026)
Pack: Common Bayesian Settings
A "pack" bundles the most common prior and LR patterns for a domain.
| Setting | Common prior | Common LR failure |
|---|---|---|
| Medical screening | Population prevalence | Treating sensitivity as posterior |
| Security alert triage | Fraction of alerts that were real | "Matches signature" = "is the threat" |
| Hiring | Historical fraction of great hires in this role | One strong interview = "great candidate" |
| A/B test | Prior probability the change has a real effect | "p < 0.05" without prior |
Contribute a pack for your domain — see the template at the repo root.
→ Sources: references/sources.md
Common Rationalizations
Note — [D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.
| Fake move | Reality |
|---|---|
| [D] "This looks just like X" | Not enough. How often does it look like X when it isn't? Without LR, you have rhetoric. |
| [D] Ignoring base rate because "this case feels different" | The base rate captures everything you don't know. "Feels different" is already included. |
| [D] Confusing P(E|H) with P(H|E) | The prosecutor's fallacy. They can differ by orders of magnitude. |
| [D] Treating correlated evidence as independent | Multiplying correlated likelihoods overstates the update. Identify common causes. |
| [D] Updating intuitively without numbers | Intuitive updates are miscalibrated — too strong on confirming, too weak on disconfirming. |
| [D] Picking the prior after seeing the evidence | Hindsight contamination. Commit to the prior before evidence. |
| [D] "I'm doing a Bayesian update" without naming P(H), P(E|H), P(E|not-H) | Then you are not. You are using the word. |
| [D] Absence of evidence = evidence of absence | Depends on P(E|H) and P(E|not-H). If evidence is rarely observable, absence is weak. |
| [D] Posterior keeps drifting toward H every round without calibration check | Either H is increasingly likely or there is a confirmation-bias leak. |
| To add [O] entries: paste a real failure instance here after each production use | Description of what happened |
Red Flags
- Prior never named · "Consistent with X" treated as proof of X · Sensitivity without false-positive rate · Correlated evidence summed as independent · Posterior matches vivid story not math · "Highly likely" without a number · Prior chosen after evidence · "Bayesian" invoked without an actual update
Verification
- Hypothesis and alternatives explicitly named and exhaustive
- Prior named with a number and source, before evidence is examined
- P(E|H) AND P(E|not-H) both estimated · Likelihood ratio computed
- Posterior computed numerically · Independence of evidence pieces checked
- Decision threshold and action stated · Next evidence identified
Part of deciqAI Knowledge Skills — 237 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/s/bayesian-reasoning · Built by deciqAI · github.com/deciqAI · Contributions welcome.
Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/bayesian-reasoning.json